How do I display real-time graphs in a simple UI for a python program?

Question

I have a complicated algorithm that updates 3 histograms that are stored in arrays. I want to debug my algorithm, so I was thinking of showing the arrays as histograms in a user interface. What is the easiest way to do this. (Rapid application development is more important than optimized code.)

I have some experience with Qt (in C++) and some experience with matplotlib.

(I'm going to leave this question open for a day or two because it's hard for me to evaluate the solutions without a lot more experience that I don't have. Hopefully, the community's votes will help choose the best answer.)

Answer 1

Edit: Nowadays, it is easier and better to use matplotlib.animation:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation


def animate(frameno):
    x = mu + sigma * np.random.randn(10000)
    n, _ = np.histogram(x, bins, normed=True)
    for rect, h in zip(patches, n):
        rect.set_height(h)
    return patches    

mu, sigma = 100, 15
fig, ax = plt.subplots()
x = mu + sigma * np.random.randn(10000)
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)

ani = animation.FuncAnimation(fig, animate, blit=True, interval=10,
                              repeat=True)
plt.show()

---

There is an example of making an animated graph here. Building on this example, you might try something like:

import numpy as np
import matplotlib.pyplot as plt

plt.ion()
mu, sigma = 100, 15
fig = plt.figure()
x = mu + sigma*np.random.randn(10000)
n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
for i in range(50):
    x = mu + sigma*np.random.randn(10000)
    n, bins = np.histogram(x, bins, normed=True)
    for rect,h in zip(patches,n):
        rect.set_height(h)
    fig.canvas.draw()

I can get about 14 frames per second this way, compared to 4 frames per second using the code I first posted. The trick is to avoid asking matplotlib to draw complete figures. Instead call plt.hist once, then manipulate the existing matplotlib.patches.Rectangles in patches to update the histogram, and call fig.canvas.draw() to make the updates visible.